Generic agent harnesses will not stay ahead of long-horizon RL-trained models, argues Ruslan
ruslansv · x · 2026-07-27
The author argues that the idea of a sufficiently sophisticated generic harness staying competitive with models post-trained or RL-trained on long-horizon agent trajectories is copium.
In their view, generic scaffolding still matters for:
- organization-specific distributions the labs never saw
- rapid adaptation between model releases
But it is not a durable substitute for models that have already absorbed core long-horizon behaviors. They compare the claim to believing prompt engineering could permanently outrun better base models.
Related event: General Agent Harnesses Fall Short Against Long-Horizon RL Models(2 posts)→
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